With industries ranging from IoT to manufacturing generating and collecting a constantly increasing amount of time-series data, the growth of the time-series database (TSDB) market over the past five years has not come as a surprise. This popularity has resulted in a large number of time series DB solutions coming on the market, sometimes making it difficult to choose the best time series database for a certain business scenario. This article compares two popular time series DBMS products – InfluxDB and TDengine – to help you determine which is right for your use case.
InfluxDB is one of the earliest of the new generation of open-source time-series databases and as such is one of the most commonly found in existing deployments. Its large community and documentation resources are major benefits, but it can suffer from slow performance in some conditions, such as when data sets have high cardinality. TDengine is also an open-source time-series database that differentiates itself with high performance, a distributed cloud-native architecture, and built-in caching, data subscription, and stream processing that simplify the overall system design.
The following table compares the basic information of InfluxDB vs. TDengine.
Basic Info: InfluxDB vs. TDengine
| InfluxDB | TDengine | |
|---|---|---|
| Creator | InfluxData | TDengine |
| Initial release | 2013 | 2017 |
| Main development language | Go | C |
| Main query language | Flux (proprietary) | Standard SQL |
| License | MIT | AGPL |
| Operating systems | Linux, macOS, and Windows | Linux, macOS, and Windows |
The following table indicates specific features supported by InfluxDB and TDengine.
Feature Set: InfluxDB vs. TDengine
| Feature | InfluxDB | TDengine |
|---|---|---|
| Private deployment | ✅ | ✅ |
| Scalability | ✅ | ✅ |
| System connection management | ✅ | ✅ |
| Query task management | ✅ | ✅ |
| Data import | ✅ | ✅ |
| Data export | ✅ | ✅ |
| Multi-layer storage | ✅ | ✅ |
| Telegraf data collection | ✅ | ✅ |
| Grafana data visualization | ✅ | ✅ |
| REST API | ✅ | ✅ |
| C/C++ connector | ❌ | ✅ |
| JDBC support | ❌ | ✅ |
| Go connector | ✅ | ✅ |
| Python connector | ✅ | ✅ |
| Database configuration | ✅ | ✅ |
| Replica configuration | ✅ | ✅ |
| Data retention policy | ✅ | ✅ |
| Data partitioning | ✅ | ✅ |
| Stream processing | ❌ | ✅ |
| Data subscription | ❌ | ✅ |
| Microsecond precision | ✅ | ✅ |
| Aggregation | ✅ | ✅ |
| Downsampling | ✅ | ✅ |
| Limits and offsets | ✅ | ✅ |
| Interpolation | ✅ | ✅ |
| Clustering | Enterprise only | ✅ |
In conclusion, InfluxDB and TDengine both have rich feature sets and are great choices for processing time-series data. The best choice for your particular scenario may depend on your specific requirements, such as whether SQL support is necessary. If you would like to know more about how TDengine can support your enterprise time-series data processing needs, contact us at business@tdengine.com to speak with an account representative.
